Toward explainable artificial intelligence for precision pathology
The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …
diagnostic pathology with respect to its ability to analyze histological images and …
Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers
Precision medicine is critically dependent on better methods for diagnosing and staging
disease and predicting drug response. Histopathology using hematoxylin and eosin (H&E) …
disease and predicting drug response. Histopathology using hematoxylin and eosin (H&E) …
Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides
C Saillard, R Dubois, O Tchita, N Loiseau… - Nature …, 2023 - nature.com
Abstract Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key
biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is …
biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is …
A guide to artificial intelligence for cancer researchers
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …
a readily accessible tool for cancer researchers. AI-based tools can boost research …
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology
Hematoxylin-and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis
of cancer. In recent years, development of deep learning-based methods in computational …
of cancer. In recent years, development of deep learning-based methods in computational …
Slideflow: deep learning for digital histopathology with real-time whole-slide visualization
Deep learning methods have emerged as powerful tools for analyzing histopathological
images, but current methods are often specialized for specific domains and software …
images, but current methods are often specialized for specific domains and software …
[HTML][HTML] Mitosis detection, fast and slow: robust and efficient detection of mitotic figures
M Jahanifar, A Shephard, N Zamanitajeddin… - Medical Image …, 2024 - Elsevier
Counting of mitotic figures is a fundamental step in grading and prognostication of several
cancers. However, manual mitosis counting is tedious and time-consuming. In addition …
cancers. However, manual mitosis counting is tedious and time-consuming. In addition …
Open and reusable deep learning for pathology with WSInfer and QuPath
Digital pathology has seen a proliferation of deep learning models in recent years, but many
models are not readily reusable. To address this challenge, we developed WSInfer: an open …
models are not readily reusable. To address this challenge, we developed WSInfer: an open …
[HTML][HTML] Artificial intelligence-based mitosis scoring in breast cancer: Clinical application
In recent years, artificial intelligence (AI) has demonstrated exceptional performance in
mitosis identification and quantification. However, the implementation of AI in clinical …
mitosis identification and quantification. However, the implementation of AI in clinical …